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Computational Intelligence and Neuroscience
Volume 2017, Article ID 6153951, 19 pages
Research Article

AMOBH: Adaptive Multiobjective Black Hole Algorithm

1School of Automation, China University of Geosciences, Wuhan 430074, China
2Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430070, China

Correspondence should be addressed to Tao Wu; nc.ude.guc@oatuw

Received 4 June 2017; Revised 1 October 2017; Accepted 22 October 2017; Published 23 November 2017

Academic Editor: José Alfredo Hernández-Pérez

Copyright © 2017 Chong Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called “adaptive multiobjective black hole algorithm” (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.